This paper proposes a frequency security-constrained optimal restoration scheduling framework for active distribution networks (FRSDN). The approach leverages distributed energy resources (DERs), particularly the inverter-interfaced renewable energy generators (IIREGs). First, incorporating the equivalent aggregated frequency response from both the governors of synchronous thermal generators (TGs) and the virtual synchronous control manner of IIREGs, linearized and conservative constraints for dynamic frequency metrics are derived, in particular for load-pickup process in renewable-based distribution system restoration (DSR). Next, a sequential dynamic boundary identification model (SDIDM) is developed to identify the dynamic boundaries and the involved generator types of networked microgrids (MGs), which facilitates the precise imposition of frequency security constraints by conserving the serial numbers of dynamically clustered networked MGs. Finally, the frequency-constrained DSR method is formulated as a three-layer robust mixed-integer linear programming (MILP) problem and solved by the column-and-constraint generation (C&CG) algorithm. Numerical results on a modified IEEE 123-node system are leveraged to demonstrate the effectiveness and superiorities of the proposed method. It is manifested that the frequency responses of all boundary-aware MGs could be confined to secure conditions by implementing the proposed FRSDN.

In 100% renewable energy power systems (REPSs), the frequency deviation is independent of the storage and release of rotor kinetic energy, and the essence of frequency control changes from energy balance to power balance. Thus, conventional LFC schemes are no longer applicable for 100% REPSs. To realize constant frequency operation of 100% REPSs, in this article, a novel load frequency control (LFC) scheme is proposed, for the first time. Furthermore, communication delays and packet losses are also considered. The proposed LFC scheme consists of two parts: a cascaded extended state filter (CESF) and a robust decision-making model (RDMM). First, the Padé rational polynomial is utilized to approximate the delay factor, based on which the LFC model with delays of the 100% REPS is established. Next, combining the Kalman-Bucy filter and extended state observer, the CESF is proposed to accurately estimate load variations. Then, taking both the renewable energy variation and the probability of packet losses into account, the RDMM is proposed to formulate the real-time power balance scheme. Finally, the proposed LFC scheme is verified on a modified IEEE 39-bus system with a pattern of 100% REPS.

This paper proposes a deep semi-supervised numerical false data detection (DSS-NFDD) method, where hidden data in labeled and unlabeled datasets are leveraged simultaneously, to detect the false data in CPPS. Two types of false data are considered in this study: one is the forged fault data, which makes operators mistakenly believe that there is a fault in their operating system; the other is the false data used to conceal actual faults. First, a data dimension reduction method is proposed based on the PageRank algorithm to avoid the excessive noise caused by high-dimension data. Then, a semi-supervised deep learning framework is established to detect false data samples, which consists of two parts: one is the priori estimation module, and the other is a false data scoring network. A novel concentration loss function is presented for training the false data scoring network, which minimizes the impacts of noise pollution and sample bias. Next, a false feature location method is proposed to help human operators eliminate anomalies. Finally, the effectiveness and the superiorities of the proposed DSS-NFDD method are verified by analyzing the simulation results for the IEEE-39 bus and IEEE-118 bus systems.

Regarding the issue that the short-circuit ratio (SCR) has inapplicability in evaluating the voltage support effect of voltage source converters (VSCs), this letter presents the definition and calculation method of voltage stiffness (VS) for strength evaluation of VSC-penetrated power systems. Theoretical and simulation analyses are performed to verify the effectiveness of the proposed VS.

The pole-to-ground fault is one of the most typical faults in modular multilevel converters (MMCs) based DC grid. Analyzing the transient characteristics of fault voltages and currents is of great significance for fault identification, configuration of protection, and optimization of system parameters. This paper proposes an equivalent transient characteristics calculation method for pole-to-ground faults for symmetrical monopolar MMC based DC grid. Firstly, according to the mechanism of the pole-to-ground faults, the fault network and the steady-state network are formed. Then, the star-delta transformation is performed on modelling the MMCs in the fault network, which overcomes the problem that the number of the inserted sub-modules (SMs) in each arm is time-variant. Based on the discrete adjoint model, the pre-fault and faulted matrices describing the voltages and currents are established, and a general method for solving the transient voltages and currents is proposed. Finally, the proposed method is validated in a four-terminal symmetrical monopolar MMC based DC grid. The results show that the proposed method is of high accuracy, and is much more time efficient than time-domain simulation.

This article proposes a hybrid data-model driven dynamic frequency trajectory (DFT) prediction method for achieving an accurate perception of low-inertia and weak-damping power systems. First, an improved primary frequency regulation (PFR) model is established for calculating the dominant oscillation mode. Different from the existing PFR models, the proposed PFR model has fully considered the spatial frequency distribution of weak-damping power systems. Then, a novel neural network topology based on edge aggregated graph attention networks (EGAT) and long short-term memory (LSTM) is proposed for the online prediction of maximum and steady-state frequency deviations, by which the temporal features of frequency can be accurately extracted. In addition, multiple EGAT-LSTM units with various input time windows of different lengths are cascaded to boost the proposed prediction accuracy. Next, a hybrid data-model driven DFT prediction method is proposed, which is based on the improved PFR model and the proposed EGAT-LSTM model. Finally, the proposed method is verified on the IEEE 39-bus system. The numerical results demonstrate that the proposed method can accurately predict the DFT, which outperforms the existing solution methods.